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pmid:36532037
Construction and validation of a robust prognostic model based on immune features in sepsis.
Yongxin Zheng · Baiyun Liu · Xiumei Deng · Yubiao Chen · Yongbo Huang · Yu Zhang · Yonghao Xu · Ling Sang · Xiaoqing Liu · Yimin Li
Frontiers in immunology · 2022 doi:10.3389/fimmu.2022.994295
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Complete file
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Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.\nMaterials and methods: The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.\nResults: A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).\nConclusions: Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.", "claims": [{"id": "claim-method", "statement": "The prognostic prediction model was constructed by univariate and multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36532037", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "The prognostic prediction model was constructed by univariate and multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE65682", "GSE63062", "GSE95233", "GSE106878", "GSE63042", "E-MTAB-4451", "E-MTAB-5273", "E-MTAB-5274"], "resolved_accessions": ["GSE65682", "GSE63062", "GSE95233", "GSE106878", "GSE63042", "E-MTAB-4451", "E-MTAB-5273", "E-MTAB-5274"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."], "basis": "The paper names GSE65682, GSE63062, GSE95233, GSE106878, GSE63042, E-MTAB-4451, E-MTAB-5273, E-MTAB-5274 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36532037", "data:geo:E-MTAB-4451"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-4451"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:20:19Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14958, "completion_tokens": 2857, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36532037", "type": "paper", "label": "pmid:36532037"}, {"id": "source:pmid:36532037", "type": "manuscript", "label": "PMID 36532037 full text"}, {"id": "data:geo:E-MTAB-4451", "type": "dataset", "label": "GEO E-MTAB-4451"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36532037", "type": "paper", "label": "pmid:36532037"}, {"id": "source:pmid:36532037", "type": "manuscript", "label": "PMID 36532037 full text"}, {"id": "data:geo:E-MTAB-4451", "type": "dataset", "label": "GEO E-MTAB-4451"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36532037", "type": "extraction", "used": ["source:pmid:36532037"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-4451", "type": "accession_resolution", "used": ["data:geo:E-MTAB-4451"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36532037", "parent_hash": "sha256:3295dc94052230a906c97d78fc5edafdb9827d12209c71b16efcf4f2992606f2", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:E-MTAB-4451 licence not read", "asset:data:geo:E-MTAB-4451 not downloaded", "asset:data:geo:E-MTAB-5273 licence not read", "asset:data:geo:E-MTAB-5273 not downloaded", "asset:data:geo:E-MTAB-5274 licence not read", "asset:data:geo:E-MTAB-5274 not downloaded", "asset:data:geo:GSE106878 licence not read", "asset:data:geo:GSE106878 not downloaded", "asset:data:geo:GSE63042 licence not read", "asset:data:geo:GSE63042 not downloaded", "asset:data:geo:GSE63062 licence not read", "asset:data:geo:GSE63062 not downloaded", "asset:data:geo:GSE65682 licence not read", "asset:data:geo:GSE65682 not downloaded", "asset:data:geo:GSE95233 licence not read", "asset:data:geo:GSE95233 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The prognostic prediction model was constructed by univariate and multivariate Cox regression analysis."], "order_basis": "analysis_only", "id": "assay:1"}]}